Senior RWD Scientist

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Senior Scientist, Real-World Data & Evidence Foundations

Data, Methods & Solutions

• Patient-Focused Real-World Evidence (PRWE)
  
  

Role Overview

The Senior Scientist, Real-World Data & Evidence Foundations builds the software our scientists use to run real-world evidence analyses more efficiently, and makes sure the answers are right. These are epidemiology and statistics applications: cohort builders, incidence and prevalence estimates, treatment-pattern analyses, descriptive study reports. A tool like that can run perfectly and still be wrong, if a lookback window is off or a denominator is incorrectly defined. So this role pairs software development with enough epidemiology and statistics to know what a correct result looks like. This person owns each tool’s analytical logic, its test sets and its acceptance criteria, and builds the tools themselves with agentic coding tools. They build on the company’s shared AI and data platforms, which the data science and IT teams run.

Key Responsibilities

Real-World Evidence Tools

Build epidemiology and statistics applications that scientists use directly, from a simple front end to the analysis code behind it.

Implements each tool’s analytical logic: cohort entry, lookback and follow-up windows, denominators and statistical methods, agreed with the RWE scientists who will use the output.

Write the known-answer test sets and acceptance criteria every tool must pass before release, drawn from published studies and past analyses, and rerun them whenever the data or the code changes.

Turn validated analyses and agentic workflows that work for one person into tools the function can use, for tasks like literature screening or drafting code lists, with a person checking the output before it is used.

Use agentic coding tools such as Claude Code routinely, and review what they produce as closely as a colleague’s work.

Align tools to the appropriate platforms and workstreams required (exploratory vs. regulatory submissions).

Work with the data science and IT teams that run the shared AI and data platforms when a tool needs enterprise deployment.

Work within company AI governance, and keep patient-level and licensed data out of any tool not approved for it.

Real-World Data Foundation

Build on the curated, analysis-ready data, standard cohorts and code lists that the real-world data curation team maintains

Apply the terms of each data license and data use agreement, including access controls, small-cell suppression and output checks.

Scientific & Strategic Leadership

Support the design of standardized workflows which support complex observational studies and analytical strategies.

Good understanding on how to frame research questions and define fit-for-purpose evidence approaches.

Contribute standardized workflows for evidence strategies that support development, regulatory and broader product lifecycle decisions.

Anticipate methodological and data risks and propose practical solutions.

Decision Enablement

Translate analyses into clear, actionable insights for decision-making.

Support study- and program-level decisions on evidence generation, data selection and analytical strategy.

Help ensure evidence is relevant to the question being asked and the decision it is meant to support.

Cross-Functional Collaboration

Collaborate across clinical development, biostatistics, regulatory, medical, HEOR, data science and IT.

Work with IT and the enterprise data, AI and platform teams, who own the shared infrastructure, so tools meet company architecture and security standards.

Build trusted working relationships that make execution and alignment easier.

Minimum Qualifications

PhD, or a Master’s degree with equivalent experience, in epidemiology, biostatistics, statistics, computer science, data science or a related quantitative field.

8+ years in real-world evidence, epidemiology, biostatistics or health data science, including hands-on development of analytical software that other people use.

Hands-on work with claims, EHR or registry data, and knowledge of how the way it is collected shapes confounding, missingness and misclassification.

Working knowledge of the epidemiological and statistical methods used in observational research, such as cohort designs, incidence and prevalence, survival analysis and confounding adjustment.

Strong Python and SQL, written with software engineering habits such as Git, automated testing and code review. Enough R to read and port analysts’ code.

Regular hands-on use of agentic AI coding tools such as Claude Code on real projects, with the testing and review habits that keep the output trustworthy.

Clear communication of analytical and technical choices to scientists and decision makers.

Preferred Qualifications

Turning a validated analysis program into a reusable tool.

OMOP CDM and OHDSI analytics tools.

Building or evaluating LLM-based agents or workflows.

Modern web front-end development.

Experience with Israeli health data sources.

Computerized system validation (GxP) or other regulated software practice.

A publication record or open-source contributions in real-world evidence methods or tooling.

What we offer: We are curious minds that come from a broad range of backgrounds, perspectives, and life experiences. We believe that this variety drives excellence and innovation, strengthening our ability to lead in science and technology. We are committed to creating access and opportunities for all to develop and grow at your own pace. Join us in building a culture of inclusion and belonging that impacts millions and empowers everyone to work their magic and champion human progress!

Apply now and become a part of a team that is dedicated to Sparking Discovery and Elevating Humanity!